Federated Learning.
In the frame of Federated learning [1]}, clients (e.g. smartphones) only transmit the gradients of all model parameters, which are obtained from training local data of the clients, to the server. The server then aggregates the gradients with weighted average and update the server model with the combined gradients, \(w_{t+1} \leftarrow w_t - \eta \sum _{k = 1}^{K}\frac{n_k}{n}*g_k\) , where \(n_k\)  is the number of samples for client \(k\) , \(\eta \)  is learning rate, \(w_t\)  indicates the model parameters at round \(t\)  and \(g_k\)  denotes the gradients from client \(k\) . After that, the server delivers the updated model to all clients for the next iterations. In another word, federated learning does not share data but only shares model parameters [2]}. Thus, it reduces the privacy and security risks by containing the raw data within clients’ devices [1]}. In addition, there are a few variants and improvements based on the fundamental federated learning algorithm.
